The 0.1% SOTA Intelligence Engine.
Deterministic mathematical breakdowns, Triton kernel replications, and verified benchmark deltas for principal AI architects, ML teams, and quantitative funds.
Today's Highest Scoring Alpha
Dual-Chunk Recurrent Linear Attention: Sub-Quadratic Scaling at 128k Context
Replaces standard quadratic multi-head attention with a dual-chunk recurrent linear attention kernel. Achieves 3.2x higher decode throughput and reduces KV-cache memory from 18.4 GB to 1.9 GB on a 70B parameter model at 128k context, with zero accuracy degradation on SWE-bench Verified.
Inference Cost & Throughput ROI Calculator
Compare commercial LLM APIs vs unoptimized cloud clusters vs the ARGMAX SOTA stack.
The Attention Architecture Zoo
Compare KV-cache memory scaling and algorithmic complexity across 5 frontier mechanisms.
FRONTIER SOTA: Merges local quadratic sharpness with global O(1) linear recurrence.
Model VRAM & Cloud GPU Cost Estimator
Simulate exact KV-cache memory envelopes, quantization trade-offs, and cloud operational budgets.
The ARGMAX Research Standard
How our research lab filters 500+ daily papers, verifies mathematical derivations, and certifies production kernels.
Frontier Radar & Paper Curation
Every day, our research desk monitors 500+ preprints and open releases across arXiv (cs.AI, cs.LG, cs.CL, stat.ML), GitHub star velocity trackers, and frontier lab disclosures (DeepMind, OpenAI, Anthropic, Meta FAIR, DeepSeek). We filter out 99.9% of derivative publications to isolate true architectural breakthroughs.
Zero-Hallucination Math Audits
No hand-waving abstractions or marketing summaries. Every mathematical derivation, tensor dimension contraction, and complexity claim is verified against primary source proofs.
Hardware GPU Benchmarking
We benchmark real-world decode throughput, latency spikes, and peak KV-cache memory envelopes across enterprise GPU clusters (NVIDIA H100 SXM, H200, B200) instead of relying on synthetic claims.
Production PyTorch & Triton Kernels
Before any teardown is published, our engineering lab compiles and executes the tensor forward pass inside an isolated verification sandbox. We test numerical stability, tensor shape boundaries, and backprop gradient norms, shipping ready-to-deploy Triton kernels and Google Colab environments.